Finding normal probabilities
|
|
- Nicholas Bridges
- 7 years ago
- Views:
Transcription
1 Finding normal probabilities A survey shows that women have a mean height of µ = 65 inches and a standard deviation of σ = 3.5 inches. For a randomly chosen woman, what is the probability that her height is less than 70 inches? Step 1: Translate the height x = 70 into an equivalent z-score by the formula z = x µ σ = = Step 2: Look up the answer in the normal probability table (Table A of appendix). In this case, z = 1.43 corresponds to a probability
2 Standard Normal Cumulative Probabilities z
3 Standard Normal Cumulative Probabilities z
4 So a fraction.9236 (just over 92%) of the women have heights that are 70 inches or less. If the question was what percentage of women have a height over 70 inches? we would first calculate the percentage less than 70 inches, and then subtract from 100%. So the answer here is about 8%. 4
5 Here is another example: For mean, the mean height is 70 inches and the standard deviation is 4 inches. What proportion of men are between 64 and 72 inches? 5
6 Solution: For x = 72, we calculate z = = 0.5. But corresponding to 0.5, the probability is.6915, i.e % of men are of height 72 inches or less. For x = 64, we calculate z = = 1.5. This corresponds to a probability of.0668, i.e. 6.68% of men are of height 64 inches or less. The difference, =.6247 or about 62.5%, represents the proportion of men whose height lie between 64 and 72 inches. 6
7 Another type of question is given the probability, find the value that this corresponds to. For example: In the SAT the mean is 500 and the standard deviation is 100. What score do you have to get to be in the top 15% of all students? 7
8 In this case, we need to know the z-score corresponding to an area under the curve of 0.85 (0.85 being the left-tail probability, i.e where 0.15 is the right-tail probability). By looking up the table, the answer is 1.04 (to two decimal places, e.g. for z = 1.03, 1.04, 1.05 we get probabilities , , is the closest to exactly 0.85 and this corresponds to a z = 1.04). Thus z = x µ = x 500 σ 100 Translates to an actual score of = x = =
9 Using z-scores to compare distributions on different scales An example: events Comparing performances of athletes in different In the 2005 World Track and Field Championships in Helsinki, the women s javelin event was won in m. (world record) while the women s high jump event was won in 2.02 m. Which was the better performance? 9
10 One way to judge this is to convert both performances to a standardized score by the formula where z = x x s x is the actual performance of a given athlete in an event x is the mean over all athletes in that event s is the SD over all athletes in that event For multi-event competitions such as the men s decathlon or the women s heptathlon, we might also consider adding the z- scores over different events, to create a combined score for each athlete. 10
11 A simpified but realistic example: Three athlete compete in the 100 m dash, the shot put and the long jump. Shown are their results, and also the means and standard deviations over many competitions. Who should get the gold medal, and which one performance stands out as the best in the competition? Competitor 100 m Shot Put Long Jump A 10.1 sec B 9.9 sec C 10.3 sec Mean 10.0 sec SD 0.2 sec
12 Compute the z scores Competitor 100 m Shot Put Long Jump A B C So C s performance in the long jump is the best of the whole competition. But B s total score (2.5) deserves the gold medal ahead of A (1.5) and C (2.0). 12
13 A more complicated example of normal probability calculations Part 1. A manufacturer of washing powder sells its powder in 32 ounce cartons. However the machine that fills the cartons is not precisely accurate assume that the amount actually delivered to the carton has a mean that can be set by the manufacturer, but the standard deviation is 0.3 ounces. Assume the curve is normal. If the machine was set to deliver (a mean of) exactly 32 ounces, half the cartons would be underweight. To avoid this,the company sets the mean to be 32.5 ounces. In that case, what proportion of cartons sold are underweight? 13
14 Solution. The z-score is z = = From the normal tables, the area under the curve, to the left of , is about Therefore, under this regime, about 4.8% of all cartons sold are underweight. 14
15 Part 2. The company lawyers advise that 4.8% underweight cartons is too many, and advise it should be no more than 2%. To achieve this, what should the setting of the machine be? Solution. Using the table, the z value associated with a 2% area under the curve is z = Therefore, we have to solve for µ in the equation Hence z = 32 µ σ = 32 µ 0.3 = µ = = The machine should be set to ounces. 15
16 Part 3. The company president says this is giving away too much powder and instructs that the machine setting be no higher than 32.3 ounces. To achieve the 2% rate of underweight boxes, the standard deviation σ will have to be reduced. What value of σ is needed to comply with both the company president s and the lawyers requirement? Answer. We still have z = 2.05, but now we fix µ = Solve z = We find σ = = σ =
17 The Binomial Distribution Example. Suppose we have a large number of cards each marked with one of the letters A,B,C,D,E. I draw five cards (independently) from this deck and ask someone to guess which letter is on each of them. What is the probability that they get exactly two of them correct? Context: This is a model for an ESP experiment (p. 269 of text). If someone guesses the right answer more often than they should do by chance, this is sometimes taken as evidence of ESP. However, maybe they just got lucky we can assess this better if we know the probabilities of different outcomes that might occur by chance. 17
18 In this case let s label the possible outcomes of the individual experiments either S (success) or F (failure). How many ways can we do this five times and get exactly two S s? SSFFF SFFSF FSSFF FSFFS FFSFS SFSFF SFFFS FSFSF FFSSF FFFSS There are 10 ways to do it. Each of these has the same probability, which is = Therefore the answer is =
19 This is not an especially small probability. But suppose the person got it right 4 times out of 5. In this case the possible orders are SSSSF SSSFS SSFSS SFSSS FSSSS Five possible outcomes, each with a probability = The overall probability is = This seems small enough to be suspicious maybe he or she really does have ESP! 19
DETERMINE whether the conditions for a binomial setting are met. COMPUTE and INTERPRET probabilities involving binomial random variables
1 Section 7.B Learning Objectives After this section, you should be able to DETERMINE whether the conditions for a binomial setting are met COMPUTE and INTERPRET probabilities involving binomial random
More informationBinomial Random Variables. Binomial Distribution. Examples of Binomial Random Variables. Binomial Random Variables
Binomial Random Variables Binomial Distribution Dr. Tom Ilvento FREC 8 In many cases the resonses to an exeriment are dichotomous Yes/No Alive/Dead Suort/Don t Suort Binomial Random Variables When our
More informationLesson 7 Z-Scores and Probability
Lesson 7 Z-Scores and Probability Outline Introduction Areas Under the Normal Curve Using the Z-table Converting Z-score to area -area less than z/area greater than z/area between two z-values Converting
More informationProbability Distributions
Learning Objectives Probability Distributions Section 1: How Can We Summarize Possible Outcomes and Their Probabilities? 1. Random variable 2. Probability distributions for discrete random variables 3.
More information6.4 Normal Distribution
Contents 6.4 Normal Distribution....................... 381 6.4.1 Characteristics of the Normal Distribution....... 381 6.4.2 The Standardized Normal Distribution......... 385 6.4.3 Meaning of Areas under
More informationMEASURES OF VARIATION
NORMAL DISTRIBTIONS MEASURES OF VARIATION In statistics, it is important to measure the spread of data. A simple way to measure spread is to find the range. But statisticians want to know if the data are
More informationStatistics E100 Fall 2013 Practice Midterm I - A Solutions
STATISTICS E100 FALL 2013 PRACTICE MIDTERM I - A SOLUTIONS PAGE 1 OF 5 Statistics E100 Fall 2013 Practice Midterm I - A Solutions 1. (16 points total) Below is the histogram for the number of medals won
More informationPoint and Interval Estimates
Point and Interval Estimates Suppose we want to estimate a parameter, such as p or µ, based on a finite sample of data. There are two main methods: 1. Point estimate: Summarize the sample by a single number
More informationFirst Midterm Exam (MATH1070 Spring 2012)
First Midterm Exam (MATH1070 Spring 2012) Instructions: This is a one hour exam. You can use a notecard. Calculators are allowed, but other electronics are prohibited. 1. [40pts] Multiple Choice Problems
More informationMULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. A) 0.4987 B) 0.9987 C) 0.0010 D) 0.
Ch. 5 Normal Probability Distributions 5.1 Introduction to Normal Distributions and the Standard Normal Distribution 1 Find Areas Under the Standard Normal Curve 1) Find the area under the standard normal
More informationBinomial Random Variables
Binomial Random Variables Dr Tom Ilvento Department of Food and Resource Economics Overview A special case of a Discrete Random Variable is the Binomial This happens when the result of the eperiment is
More information6 3 The Standard Normal Distribution
290 Chapter 6 The Normal Distribution Figure 6 5 Areas Under a Normal Distribution Curve 34.13% 34.13% 2.28% 13.59% 13.59% 2.28% 3 2 1 + 1 + 2 + 3 About 68% About 95% About 99.7% 6 3 The Distribution Since
More informationThe Math. P (x) = 5! = 1 2 3 4 5 = 120.
The Math Suppose there are n experiments, and the probability that someone gets the right answer on any given experiment is p. So in the first example above, n = 5 and p = 0.2. Let X be the number of correct
More informationNormal distribution. ) 2 /2σ. 2π σ
Normal distribution The normal distribution is the most widely known and used of all distributions. Because the normal distribution approximates many natural phenomena so well, it has developed into a
More information5/31/2013. 6.1 Normal Distributions. Normal Distributions. Chapter 6. Distribution. The Normal Distribution. Outline. Objectives.
The Normal Distribution C H 6A P T E R The Normal Distribution Outline 6 1 6 2 Applications of the Normal Distribution 6 3 The Central Limit Theorem 6 4 The Normal Approximation to the Binomial Distribution
More informationThe Binomial Probability Distribution
The Binomial Probability Distribution MATH 130, Elements of Statistics I J. Robert Buchanan Department of Mathematics Fall 2015 Objectives After this lesson we will be able to: determine whether a probability
More informationSection 1.3 Exercises (Solutions)
Section 1.3 Exercises (s) 1.109, 1.110, 1.111, 1.114*, 1.115, 1.119*, 1.122, 1.125, 1.127*, 1.128*, 1.131*, 1.133*, 1.135*, 1.137*, 1.139*, 1.145*, 1.146-148. 1.109 Sketch some normal curves. (a) Sketch
More informationThe Normal Distribution
Chapter 6 The Normal Distribution 6.1 The Normal Distribution 1 6.1.1 Student Learning Objectives By the end of this chapter, the student should be able to: Recognize the normal probability distribution
More informationLecture 2: Discrete Distributions, Normal Distributions. Chapter 1
Lecture 2: Discrete Distributions, Normal Distributions Chapter 1 Reminders Course website: www. stat.purdue.edu/~xuanyaoh/stat350 Office Hour: Mon 3:30-4:30, Wed 4-5 Bring a calculator, and copy Tables
More information8. THE NORMAL DISTRIBUTION
8. THE NORMAL DISTRIBUTION The normal distribution with mean μ and variance σ 2 has the following density function: The normal distribution is sometimes called a Gaussian Distribution, after its inventor,
More informationWeek 4: Standard Error and Confidence Intervals
Health Sciences M.Sc. Programme Applied Biostatistics Week 4: Standard Error and Confidence Intervals Sampling Most research data come from subjects we think of as samples drawn from a larger population.
More informationDensity Curve. A density curve is the graph of a continuous probability distribution. It must satisfy the following properties:
Density Curve A density curve is the graph of a continuous probability distribution. It must satisfy the following properties: 1. The total area under the curve must equal 1. 2. Every point on the curve
More informationTest 4 Sample Problem Solutions, 27.58 = 27 47 100, 7 5, 1 6. 5 = 14 10 = 1.4. Moving the decimal two spots to the left gives
Test 4 Sample Problem Solutions Convert from a decimal to a fraction: 0.023, 27.58, 0.777... For the first two we have 0.023 = 23 58, 27.58 = 27 1000 100. For the last, if we set x = 0.777..., then 10x
More informationIntroduction to Hypothesis Testing
I. Terms, Concepts. Introduction to Hypothesis Testing A. In general, we do not know the true value of population parameters - they must be estimated. However, we do have hypotheses about what the true
More informationIndependent samples t-test. Dr. Tom Pierce Radford University
Independent samples t-test Dr. Tom Pierce Radford University The logic behind drawing causal conclusions from experiments The sampling distribution of the difference between means The standard error of
More information5.4 Solving Percent Problems Using the Percent Equation
5. Solving Percent Problems Using the Percent Equation In this section we will develop and use a more algebraic equation approach to solving percent equations. Recall the percent proportion from the last
More informationBinomial Probability Distribution
Binomial Probability Distribution In a binomial setting, we can compute probabilities of certain outcomes. This used to be done with tables, but with graphing calculator technology, these problems are
More informationChapter Study Guide. Chapter 11 Confidence Intervals and Hypothesis Testing for Means
OPRE504 Chapter Study Guide Chapter 11 Confidence Intervals and Hypothesis Testing for Means I. Calculate Probability for A Sample Mean When Population σ Is Known 1. First of all, we need to find out the
More informationA Short Guide to Significant Figures
A Short Guide to Significant Figures Quick Reference Section Here are the basic rules for significant figures - read the full text of this guide to gain a complete understanding of what these rules really
More informationChapter 3. The Normal Distribution
Chapter 3. The Normal Distribution Topics covered in this chapter: Z-scores Normal Probabilities Normal Percentiles Z-scores Example 3.6: The standard normal table The Problem: What proportion of observations
More information5) The table below describes the smoking habits of a group of asthma sufferers. two way table ( ( cell cell ) (cell cell) (cell cell) )
MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Determine which score corresponds to the higher relative position. 1) Which score has a better relative
More information$2 4 40 + ( $1) = 40
THE EXPECTED VALUE FOR THE SUM OF THE DRAWS In the game of Keno there are 80 balls, numbered 1 through 80. On each play, the casino chooses 20 balls at random without replacement. Suppose you bet on the
More informationCURVE FITTING LEAST SQUARES APPROXIMATION
CURVE FITTING LEAST SQUARES APPROXIMATION Data analysis and curve fitting: Imagine that we are studying a physical system involving two quantities: x and y Also suppose that we expect a linear relationship
More informationThe Normal Distribution
The Normal Distribution Continuous Distributions A continuous random variable is a variable whose possible values form some interval of numbers. Typically, a continuous variable involves a measurement
More informationCALCULATIONS & STATISTICS
CALCULATIONS & STATISTICS CALCULATION OF SCORES Conversion of 1-5 scale to 0-100 scores When you look at your report, you will notice that the scores are reported on a 0-100 scale, even though respondents
More informationName: Date: Use the following to answer questions 2-3:
Name: Date: 1. A study is conducted on students taking a statistics class. Several variables are recorded in the survey. Identify each variable as categorical or quantitative. A) Type of car the student
More information5.1 Identifying the Target Parameter
University of California, Davis Department of Statistics Summer Session II Statistics 13 August 20, 2012 Date of latest update: August 20 Lecture 5: Estimation with Confidence intervals 5.1 Identifying
More information4. Continuous Random Variables, the Pareto and Normal Distributions
4. Continuous Random Variables, the Pareto and Normal Distributions A continuous random variable X can take any value in a given range (e.g. height, weight, age). The distribution of a continuous random
More informationNormal and Binomial. Distributions
Normal and Binomial Distributions Library, Teaching and Learning 14 By now, you know about averages means in particular and are familiar with words like data, standard deviation, variance, probability,
More informationAP STATISTICS 2010 SCORING GUIDELINES
2010 SCORING GUIDELINES Question 4 Intent of Question The primary goals of this question were to (1) assess students ability to calculate an expected value and a standard deviation; (2) recognize the applicability
More informationDef: The standard normal distribution is a normal probability distribution that has a mean of 0 and a standard deviation of 1.
Lecture 6: Chapter 6: Normal Probability Distributions A normal distribution is a continuous probability distribution for a random variable x. The graph of a normal distribution is called the normal curve.
More informationPERCENTS. Percent means per hundred. Writing a number as a percent is a way of comparing the number with 100. For example: 42% =
PERCENTS Percent means per hundred. Writing a number as a percent is a way of comparing the number with 100. For example: 42% = Percents are really fractions (or ratios) with a denominator of 100. Any
More informationIntroduction to Statistics for Psychology. Quantitative Methods for Human Sciences
Introduction to Statistics for Psychology and Quantitative Methods for Human Sciences Jonathan Marchini Course Information There is website devoted to the course at http://www.stats.ox.ac.uk/ marchini/phs.html
More information7. Normal Distributions
7. Normal Distributions A. Introduction B. History C. Areas of Normal Distributions D. Standard Normal E. Exercises Most of the statistical analyses presented in this book are based on the bell-shaped
More informationWelcome to Basic Math Skills!
Basic Math Skills Welcome to Basic Math Skills! Most students find the math sections to be the most difficult. Basic Math Skills was designed to give you a refresher on the basics of math. There are lots
More informationSection 6C Commission, Interest, Tax, Markup and Discount
Section 6C Commission, Interest, Tax, Markup and Discount In the last section, we looked at percent conversions and solving simple percent problems with a proportion. We are now going to look at some more
More informationPreliminary Mathematics
Preliminary Mathematics The purpose of this document is to provide you with a refresher over some topics that will be essential for what we do in this class. We will begin with fractions, decimals, and
More informationUniversity of Nevada Las Vegas 2015 Outdoor Track & Field COLLEGE/OPEN MEET INFORMATION
University of Nevada Las Vegas 2015 Outdoor Track & Field COLLEGE/OPEN MEET INFORMATION Please direct all meet communication to: JEBREH HARRIS Meet Director UNLV Track & Field Phone: 702-895-3985 Email:
More informationChapter 26: Tests of Significance
Chapter 26: Tests of Significance Procedure: 1. State the null and alternative in words and in terms of a box model. 2. Find the test statistic: z = observed EV. SE 3. Calculate the P-value: The area under
More informationIntroduction to the Practice of Statistics Fifth Edition Moore, McCabe
Introduction to the Practice of Statistics Fifth Edition Moore, McCabe Section 5.1 Homework Answers 5.7 In the proofreading setting if Exercise 5.3, what is the smallest number of misses m with P(X m)
More informationYou flip a fair coin four times, what is the probability that you obtain three heads.
Handout 4: Binomial Distribution Reading Assignment: Chapter 5 In the previous handout, we looked at continuous random variables and calculating probabilities and percentiles for those type of variables.
More information" Y. Notation and Equations for Regression Lecture 11/4. Notation:
Notation: Notation and Equations for Regression Lecture 11/4 m: The number of predictor variables in a regression Xi: One of multiple predictor variables. The subscript i represents any number from 1 through
More informationMath 251, Review Questions for Test 3 Rough Answers
Math 251, Review Questions for Test 3 Rough Answers 1. (Review of some terminology from Section 7.1) In a state with 459,341 voters, a poll of 2300 voters finds that 45 percent support the Republican candidate,
More informationSecond Midterm Exam (MATH1070 Spring 2012)
Second Midterm Exam (MATH1070 Spring 2012) Instructions: This is a one hour exam. You can use a notecard. Calculators are allowed, but other electronics are prohibited. 1. [60pts] Multiple Choice Problems
More informationLesson 20. Probability and Cumulative Distribution Functions
Lesson 20 Probability and Cumulative Distribution Functions Recall If p(x) is a density function for some characteristic of a population, then Recall If p(x) is a density function for some characteristic
More informationLecture 14. Chapter 7: Probability. Rule 1: Rule 2: Rule 3: Nancy Pfenning Stats 1000
Lecture 4 Nancy Pfenning Stats 000 Chapter 7: Probability Last time we established some basic definitions and rules of probability: Rule : P (A C ) = P (A). Rule 2: In general, the probability of one event
More informationChapter 4. Probability and Probability Distributions
Chapter 4. robability and robability Distributions Importance of Knowing robability To know whether a sample is not identical to the population from which it was selected, it is necessary to assess the
More informationCh. 6.1 #7-49 odd. The area is found by looking up z= 0.75 in Table E and subtracting 0.5. Area = 0.7734-0.5= 0.2734
Ch. 6.1 #7-49 odd The area is found by looking up z= 0.75 in Table E and subtracting 0.5. Area = 0.7734-0.5= 0.2734 The area is found by looking up z= 2.07 in Table E and subtracting from 0.5. Area = 0.5-0.0192
More informationCommon Multiples. List the multiples of 3. The multiples of 3 are 3 1, 3 2, 3 3, 3 4,...
.2 Common Multiples.2 OBJECTIVES 1. Find the least common multiple (LCM) of two numbers 2. Find the least common multiple (LCM) of a group of numbers. Compare the size of two fractions In this chapter,
More information1) The table lists the smoking habits of a group of college students. Answer: 0.218
FINAL EXAM REVIEW Name ) The table lists the smoking habits of a group of college students. Sex Non-smoker Regular Smoker Heavy Smoker Total Man 5 52 5 92 Woman 8 2 2 220 Total 22 2 If a student is chosen
More informationSTATISTICS 8: CHAPTERS 7 TO 10, SAMPLE MULTIPLE CHOICE QUESTIONS
STATISTICS 8: CHAPTERS 7 TO 10, SAMPLE MULTIPLE CHOICE QUESTIONS 1. If two events (both with probability greater than 0) are mutually exclusive, then: A. They also must be independent. B. They also could
More informationMind on Statistics. Chapter 8
Mind on Statistics Chapter 8 Sections 8.1-8.2 Questions 1 to 4: For each situation, decide if the random variable described is a discrete random variable or a continuous random variable. 1. Random variable
More informationStatistics 2014 Scoring Guidelines
AP Statistics 2014 Scoring Guidelines College Board, Advanced Placement Program, AP, AP Central, and the acorn logo are registered trademarks of the College Board. AP Central is the official online home
More informationMaths Workshop for Parents 2. Fractions and Algebra
Maths Workshop for Parents 2 Fractions and Algebra What is a fraction? A fraction is a part of a whole. There are two numbers to every fraction: 2 7 Numerator Denominator 2 7 This is a proper (or common)
More informationConversions between percents, decimals, and fractions
Click on the links below to jump directly to the relevant section Conversions between percents, decimals and fractions Operations with percents Percentage of a number Percent change Conversions between
More information6. Let X be a binomial random variable with distribution B(10, 0.6). What is the probability that X equals 8? A) (0.6) (0.4) B) 8! C) 45(0.6) (0.
Name: Date:. For each of the following scenarios, determine the appropriate distribution for the random variable X. A) A fair die is rolled seven times. Let X = the number of times we see an even number.
More informationKey Concept. Density Curve
MAT 155 Statistical Analysis Dr. Claude Moore Cape Fear Community College Chapter 6 Normal Probability Distributions 6 1 Review and Preview 6 2 The Standard Normal Distribution 6 3 Applications of Normal
More informationCHAPTER 6: Continuous Uniform Distribution: 6.1. Definition: The density function of the continuous random variable X on the interval [A, B] is.
Some Continuous Probability Distributions CHAPTER 6: Continuous Uniform Distribution: 6. Definition: The density function of the continuous random variable X on the interval [A, B] is B A A x B f(x; A,
More informationCharacteristics of Binomial Distributions
Lesson2 Characteristics of Binomial Distributions In the last lesson, you constructed several binomial distributions, observed their shapes, and estimated their means and standard deviations. In Investigation
More informationChapter 4. iclicker Question 4.4 Pre-lecture. Part 2. Binomial Distribution. J.C. Wang. iclicker Question 4.4 Pre-lecture
Chapter 4 Part 2. Binomial Distribution J.C. Wang iclicker Question 4.4 Pre-lecture iclicker Question 4.4 Pre-lecture Outline Computing Binomial Probabilities Properties of a Binomial Distribution Computing
More informationHaving a coin come up heads or tails is a variable on a nominal scale. Heads is a different category from tails.
Chi-square Goodness of Fit Test The chi-square test is designed to test differences whether one frequency is different from another frequency. The chi-square test is designed for use with data on a nominal
More informationChapter 5 - Practice Problems 1
Chapter 5 - Practice Problems 1 Identify the given random variable as being discrete or continuous. 1) The number of oil spills occurring off the Alaskan coast 1) A) Continuous B) Discrete 2) The ph level
More informationActivity 1: Using base ten blocks to model operations on decimals
Rational Numbers 9: Decimal Form of Rational Numbers Objectives To use base ten blocks to model operations on decimal numbers To review the algorithms for addition, subtraction, multiplication and division
More informationPre-Algebra Lecture 6
Pre-Algebra Lecture 6 Today we will discuss Decimals and Percentages. Outline: 1. Decimals 2. Ordering Decimals 3. Rounding Decimals 4. Adding and subtracting Decimals 5. Multiplying and Dividing Decimals
More informationM 1313 Review Test 4 1
M 1313 Review Test 4 1 Review for test 4: 1. Let E and F be two events of an experiment, P (E) =. 3 and P (F) =. 2, and P (E F) =.35. Find the following probabilities: a. P(E F) b. P(E c F) c. P (E F)
More informationFinancial Mathematics
Financial Mathematics For the next few weeks we will study the mathematics of finance. Apart from basic arithmetic, financial mathematics is probably the most practical math you will learn. practical in
More information6.2 Normal distribution. Standard Normal Distribution:
6.2 Normal distribution Slide Heights of Adult Men and Women Slide 2 Area= Mean = µ Standard Deviation = σ Donation: X ~ N(µ,σ 2 ) Standard Normal Distribution: Slide 3 Slide 4 a normal probability distribution
More informationThe Theory and Practice of Using a Sine Bar, version 2
The Theory and Practice of Using a Sine Bar, version 2 By R. G. Sparber Copyleft protects this document. 1 The Quick Answer If you just want to set an angle with a sine bar and stack of blocks, then take
More informationAnswer: C. The strength of a correlation does not change if units change by a linear transformation such as: Fahrenheit = 32 + (5/9) * Centigrade
Statistics Quiz Correlation and Regression -- ANSWERS 1. Temperature and air pollution are known to be correlated. We collect data from two laboratories, in Boston and Montreal. Boston makes their measurements
More informationBinomial Distribution Problems. Binomial Distribution SOLUTIONS. Poisson Distribution Problems
1 Binomial Distribution Problems (1) A company owns 400 laptops. Each laptop has an 8% probability of not working. You randomly select 20 laptops for your salespeople. (a) What is the likelihood that 5
More informationMILS and MOA A Guide to understanding what they are and How to derive the Range Estimation Equations
MILS and MOA A Guide to understanding what they are and How to derive the Range Estimation Equations By Robert J. Simeone 1 The equations for determining the range to a target using mils, and with some
More informationRevision Notes Adult Numeracy Level 2
Revision Notes Adult Numeracy Level 2 Place Value The use of place value from earlier levels applies but is extended to all sizes of numbers. The values of columns are: Millions Hundred thousands Ten thousands
More informationSTAT 200 QUIZ 2 Solutions Section 6380 Fall 2013
STAT 200 QUIZ 2 Solutions Section 6380 Fall 2013 The quiz covers Chapters 4, 5 and 6. 1. (8 points) If the IQ scores are normally distributed with a mean of 100 and a standard deviation of 15. (a) (3 pts)
More informationMATH 140 Lab 4: Probability and the Standard Normal Distribution
MATH 140 Lab 4: Probability and the Standard Normal Distribution Problem 1. Flipping a Coin Problem In this problem, we want to simualte the process of flipping a fair coin 1000 times. Note that the outcomes
More informationReview #2. Statistics
Review #2 Statistics Find the mean of the given probability distribution. 1) x P(x) 0 0.19 1 0.37 2 0.16 3 0.26 4 0.02 A) 1.64 B) 1.45 C) 1.55 D) 1.74 2) The number of golf balls ordered by customers of
More informationAn Introduction to Basic Statistics and Probability
An Introduction to Basic Statistics and Probability Shenek Heyward NCSU An Introduction to Basic Statistics and Probability p. 1/4 Outline Basic probability concepts Conditional probability Discrete Random
More informationSolving Quadratic Equations
9.3 Solving Quadratic Equations by Using the Quadratic Formula 9.3 OBJECTIVES 1. Solve a quadratic equation by using the quadratic formula 2. Determine the nature of the solutions of a quadratic equation
More informationHISTOGRAMS, CUMULATIVE FREQUENCY AND BOX PLOTS
Mathematics Revision Guides Histograms, Cumulative Frequency and Box Plots Page 1 of 25 M.K. HOME TUITION Mathematics Revision Guides Level: GCSE Higher Tier HISTOGRAMS, CUMULATIVE FREQUENCY AND BOX PLOTS
More informationSection 6.1 Discrete Random variables Probability Distribution
Section 6.1 Discrete Random variables Probability Distribution Definitions a) Random variable is a variable whose values are determined by chance. b) Discrete Probability distribution consists of the values
More informationChapter 5: Normal Probability Distributions - Solutions
Chapter 5: Normal Probability Distributions - Solutions Note: All areas and z-scores are approximate. Your answers may vary slightly. 5.2 Normal Distributions: Finding Probabilities If you are given that
More informationDescriptive statistics; Correlation and regression
Descriptive statistics; and regression Patrick Breheny September 16 Patrick Breheny STA 580: Biostatistics I 1/59 Tables and figures Descriptive statistics Histograms Numerical summaries Percentiles Human
More informationThe Normal Approximation to Probability Histograms. Dice: Throw a single die twice. The Probability Histogram: Area = Probability. Where are we going?
The Normal Approximation to Probability Histograms Where are we going? Probability histograms The normal approximation to binomial histograms The normal approximation to probability histograms of sums
More informationProbability Distributions
CHAPTER 5 Probability Distributions CHAPTER OUTLINE 5.1 Probability Distribution of a Discrete Random Variable 5.2 Mean and Standard Deviation of a Probability Distribution 5.3 The Binomial Distribution
More informationWMA RULES OF COMPETITION 2013 2016
WMA RULES OF COMPETITION 2013 2016 (Note : Rule 2 WMA RULES OF COMPETITION For ease of use, the WMA Rules of Competition additions and exceptions to the IAAF Rules are arranged to correspond to the IAAF
More informationSHELL INDUSTRIAL APTITUDE BATTERY PREPARATION GUIDE
SHELL INDUSTRIAL APTITUDE BATTERY PREPARATION GUIDE 2011 Valtera Corporation. All rights reserved. TABLE OF CONTENTS OPERATIONS AND MAINTENANCE JOB REQUIREMENTS... 1 TEST PREPARATION... 2 USE OF INDUSTRIAL
More informationSAMPLING DISTRIBUTIONS
0009T_c07_308-352.qd 06/03/03 20:44 Page 308 7Chapter SAMPLING DISTRIBUTIONS 7.1 Population and Sampling Distributions 7.2 Sampling and Nonsampling Errors 7.3 Mean and Standard Deviation of 7.4 Shape of
More informationc. Construct a boxplot for the data. Write a one sentence interpretation of your graph.
MBA/MIB 5315 Sample Test Problems Page 1 of 1 1. An English survey of 3000 medical records showed that smokers are more inclined to get depressed than non-smokers. Does this imply that smoking causes depression?
More information. 58 58 60 62 64 66 68 70 72 74 76 78 Father s height (inches)
PEARSON S FATHER-SON DATA The following scatter diagram shows the heights of 1,0 fathers and their full-grown sons, in England, circa 1900 There is one dot for each father-son pair Heights of fathers and
More informationSTA 130 (Winter 2016): An Introduction to Statistical Reasoning and Data Science
STA 130 (Winter 2016): An Introduction to Statistical Reasoning and Data Science Mondays 2:10 4:00 (GB 220) and Wednesdays 2:10 4:00 (various) Jeffrey Rosenthal Professor of Statistics, University of Toronto
More informationHypothesis Testing: Two Means, Paired Data, Two Proportions
Chapter 10 Hypothesis Testing: Two Means, Paired Data, Two Proportions 10.1 Hypothesis Testing: Two Population Means and Two Population Proportions 1 10.1.1 Student Learning Objectives By the end of this
More information